Implement real-time profile retrieval and saving in simulation API
- Added a new endpoint to retrieve real-time agent profiles during simulation, allowing users to monitor progress without going through the SimulationManager. - Enhanced the profile generation process to support real-time saving of generated profiles to specified file formats (JSON for Reddit, CSV for Twitter). - Updated the simulation configuration generator to assign appropriate agents to initial posts based on their types, improving the relevance of generated content. - Improved error handling and logging for better traceability during profile generation and retrieval processes.
This commit is contained in:
666ghj
parent
39253b3213
commit
88676e8207
4 changed files with 292 additions and 7 deletions
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@ -803,6 +803,116 @@ def get_simulation_profiles(simulation_id: str):
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}), 500
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@simulation_bp.route('/<simulation_id>/profiles/realtime', methods=['GET'])
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def get_simulation_profiles_realtime(simulation_id: str):
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"""
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实时获取模拟的Agent Profile（用于在生成过程中实时查看进度）
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与 /profiles 接口的区别：
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- 直接读取文件，不经过 SimulationManager
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- 适用于生成过程中的实时查看
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- 返回额外的元数据（如文件修改时间、是否正在生成等）
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Query参数：
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platform: 平台类型（reddit/twitter，默认reddit）
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返回：
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{
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"success": true,
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"data": {
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"simulation_id": "sim_xxxx",
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"platform": "reddit",
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"count": 15,
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"total_expected": 93, // 预期总数（如果有）
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"is_generating": true, // 是否正在生成
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"file_exists": true,
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"file_modified_at": "2025-12-04T18:20:00",
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"profiles": [...]
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}
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}
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"""
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import json
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import csv
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from datetime import datetime
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try:
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platform = request.args.get('platform', 'reddit')
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# 获取模拟目录
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sim_dir = os.path.join(Config.OASIS_SIMULATION_DATA_DIR, simulation_id)
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if not os.path.exists(sim_dir):
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return jsonify({
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"success": False,
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"error": f"模拟不存在: {simulation_id}"
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}), 404
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# 确定文件路径
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if platform == "reddit":
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profiles_file = os.path.join(sim_dir, "reddit_profiles.json")
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else:
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profiles_file = os.path.join(sim_dir, "twitter_profiles.csv")
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# 检查文件是否存在
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file_exists = os.path.exists(profiles_file)
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profiles = []
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file_modified_at = None
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if file_exists:
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# 获取文件修改时间
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file_stat = os.stat(profiles_file)
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file_modified_at = datetime.fromtimestamp(file_stat.st_mtime).isoformat()
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try:
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if platform == "reddit":
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with open(profiles_file, 'r', encoding='utf-8') as f:
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profiles = json.load(f)
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else:
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with open(profiles_file, 'r', encoding='utf-8') as f:
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reader = csv.DictReader(f)
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profiles = list(reader)
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except (json.JSONDecodeError, Exception) as e:
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logger.warning(f"读取 profiles 文件失败（可能正在写入中）: {e}")
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profiles = []
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# 检查是否正在生成（通过 state.json 判断）
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is_generating = False
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total_expected = None
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state_file = os.path.join(sim_dir, "state.json")
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if os.path.exists(state_file):
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try:
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with open(state_file, 'r', encoding='utf-8') as f:
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state_data = json.load(f)
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status = state_data.get("status", "")
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is_generating = status == "preparing"
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total_expected = state_data.get("entities_count")
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except Exception:
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pass
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return jsonify({
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"success": True,
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"data": {
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"simulation_id": simulation_id,
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"platform": platform,
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"count": len(profiles),
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"total_expected": total_expected,
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"is_generating": is_generating,
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"file_exists": file_exists,
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"file_modified_at": file_modified_at,
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"profiles": profiles
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}
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})
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except Exception as e:
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logger.error(f"实时获取Profile失败: {str(e)}")
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return jsonify({
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"success": False,
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"error": str(e),
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"traceback": traceback.format_exc()
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}), 500
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@simulation_bp.route('/<simulation_id>/config', methods=['GET'])
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def get_simulation_config(simulation_id: str):
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"""
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@ -853,7 +853,9 @@ class OasisProfileGenerator:
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use_llm: bool = True,
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progress_callback: Optional[callable] = None,
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graph_id: Optional[str] = None,
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parallel_count: int = 5
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parallel_count: int = 5,
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realtime_output_path: Optional[str] = None,
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output_platform: str = "reddit"
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) -> List[OasisAgentProfile]:
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"""
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批量从实体生成Agent Profile（支持并行生成）
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@ -864,6 +866,8 @@ class OasisProfileGenerator:
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progress_callback: 进度回调函数 (current, total, message)
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graph_id: 图谱ID，用于Zep检索获取更丰富上下文
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parallel_count: 并行生成数量，默认5
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realtime_output_path: 实时写入的文件路径（如果提供，每生成一个就写入一次）
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output_platform: 输出平台格式 ("reddit" 或 "twitter")
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Returns:
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Agent Profile列表
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@ -880,6 +884,37 @@ class OasisProfileGenerator:
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completed_count = [0] # 使用列表以便在闭包中修改
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lock = Lock()
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# 实时写入文件的辅助函数
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def save_profiles_realtime():
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"""实时保存已生成的 profiles 到文件"""
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if not realtime_output_path:
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return
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with lock:
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# 过滤出已生成的 profiles
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existing_profiles = [p for p in profiles if p is not None]
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if not existing_profiles:
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return
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try:
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if output_platform == "reddit":
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# Reddit JSON 格式
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profiles_data = [p.to_reddit_format() for p in existing_profiles]
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with open(realtime_output_path, 'w', encoding='utf-8') as f:
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json.dump(profiles_data, f, ensure_ascii=False, indent=2)
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else:
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# Twitter CSV 格式
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import csv
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profiles_data = [p.to_twitter_format() for p in existing_profiles]
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if profiles_data:
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fieldnames = list(profiles_data[0].keys())
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with open(realtime_output_path, 'w', encoding='utf-8', newline='') as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(profiles_data)
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except Exception as e:
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logger.warning(f"实时保存 profiles 失败: {e}")
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def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
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"""生成单个profile的工作函数"""
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entity_type = entity.get_entity_type() or "Entity"
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@ -936,6 +971,9 @@ class OasisProfileGenerator:
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completed_count[0] += 1
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current = completed_count[0]
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# 实时写入文件
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save_profiles_realtime()
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if progress_callback:
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progress_callback(
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current,
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@ -961,6 +999,8 @@ class OasisProfileGenerator:
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source_entity_uuid=entity.uuid,
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source_entity_type=entity_type,
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)
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# 实时写入文件（即使是备用人设）
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save_profiles_realtime()
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print(f"\n{'='*60}")
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print(f"人设生成完成！共生成 {len([p for p in profiles if p])} 个Agent")
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@ -292,7 +292,7 @@ class SimulationConfigGenerator:
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# ========== 步骤2: 生成事件配置 ==========
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report_progress(2, "生成事件配置和热点话题...")
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event_config_result = self._generate_event_config(context, simulation_requirement)
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event_config_result = self._generate_event_config(context, simulation_requirement, entities)
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event_config = self._parse_event_config(event_config_result)
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reasoning_parts.append(f"事件配置: {event_config_result.get('reasoning', '成功')}")
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@ -318,6 +318,12 @@ class SimulationConfigGenerator:
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reasoning_parts.append(f"Agent配置: 成功生成 {len(all_agent_configs)} 个")
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# ========== 为初始帖子分配发布者 Agent ==========
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logger.info("为初始帖子分配合适的发布者 Agent...")
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event_config = self._assign_initial_post_agents(event_config, all_agent_configs)
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assigned_count = len([p for p in event_config.initial_posts if p.get("poster_agent_id") is not None])
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reasoning_parts.append(f"初始帖子分配: {assigned_count} 个帖子已分配发布者")
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# ========== 最后一步: 生成平台配置 ==========
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report_progress(total_steps, "生成平台配置...")
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twitter_config = None
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@ -583,32 +589,63 @@ class SimulationConfigGenerator:
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peak_activity_multiplier=1.5
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)
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def _generate_event_config(self, context: str, simulation_requirement: str) -> Dict[str, Any]:
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def _generate_event_config(
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self,
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context: str,
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simulation_requirement: str,
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entities: List[EntityNode]
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) -> Dict[str, Any]:
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"""生成事件配置"""
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# 获取可用的实体类型列表，供 LLM 参考
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entity_types_available = list(set(
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e.get_entity_type() or "Unknown" for e in entities
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))
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# 为每种类型列出代表性实体名称
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type_examples = {}
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for e in entities:
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etype = e.get_entity_type() or "Unknown"
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if etype not in type_examples:
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type_examples[etype] = []
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if len(type_examples[etype]) < 3:
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type_examples[etype].append(e.name)
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type_info = "\n".join([
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f"- {t}: {', '.join(examples)}"
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for t, examples in type_examples.items()
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])
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prompt = f"""基于以下模拟需求，生成事件配置。
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模拟需求: {simulation_requirement}
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{context[:3000]}
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## 可用实体类型及示例
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{type_info}
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## 任务
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请生成事件配置JSON：
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- 提取热点话题关键词
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- 描述舆论发展方向
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- 设计初始帖子内容
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- 设计初始帖子内容，**每个帖子必须指定 poster_type（发布者类型）**
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**重要**: poster_type 必须从上面的"可用实体类型"中选择，这样初始帖子才能分配给合适的 Agent 发布。
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例如：官方声明应由 Official/University 类型发布，新闻由 MediaOutlet 发布，学生观点由 Student 发布。
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返回JSON格式（不要markdown）：
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{{
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"hot_topics": ["关键词1", "关键词2", ...],
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"narrative_direction": "<舆论发展方向描述>",
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"initial_posts": [
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{{"content": "帖子内容", "poster_type": "MediaOutlet"}},
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{{"content": "帖子内容", "poster_type": "实体类型（必须从可用类型中选择）"}},
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...
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],
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"reasoning": "<简要说明>"
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}}"""
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system_prompt = "你是舆论分析专家。返回纯JSON格式。"
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system_prompt = "你是舆论分析专家。返回纯JSON格式。注意 poster_type 必须精确匹配可用实体类型。"
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try:
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return self._call_llm_with_retry(prompt, system_prompt)
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@ -630,6 +667,91 @@ class SimulationConfigGenerator:
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narrative_direction=result.get("narrative_direction", "")
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)
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def _assign_initial_post_agents(
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self,
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event_config: EventConfig,
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agent_configs: List[AgentActivityConfig]
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) -> EventConfig:
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"""
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为初始帖子分配合适的发布者 Agent
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根据每个帖子的 poster_type 匹配最合适的 agent_id
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"""
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if not event_config.initial_posts:
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return event_config
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# 按实体类型建立 agent 索引
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agents_by_type: Dict[str, List[AgentActivityConfig]] = {}
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for agent in agent_configs:
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etype = agent.entity_type.lower()
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if etype not in agents_by_type:
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agents_by_type[etype] = []
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agents_by_type[etype].append(agent)
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# 类型映射表（处理 LLM 可能输出的不同格式）
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type_aliases = {
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"official": ["official", "university", "governmentagency", "government"],
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"university": ["university", "official"],
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"mediaoutlet": ["mediaoutlet", "media"],
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"student": ["student", "person"],
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"professor": ["professor", "expert", "teacher"],
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"alumni": ["alumni", "person"],
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"organization": ["organization", "ngo", "company", "group"],
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"person": ["person", "student", "alumni"],
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}
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# 记录每种类型已使用的 agent 索引，避免重复使用同一个 agent
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used_indices: Dict[str, int] = {}
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updated_posts = []
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for post in event_config.initial_posts:
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poster_type = post.get("poster_type", "").lower()
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content = post.get("content", "")
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# 尝试找到匹配的 agent
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matched_agent_id = None
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# 1. 直接匹配
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if poster_type in agents_by_type:
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agents = agents_by_type[poster_type]
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idx = used_indices.get(poster_type, 0) % len(agents)
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matched_agent_id = agents[idx].agent_id
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used_indices[poster_type] = idx + 1
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else:
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# 2. 使用别名匹配
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for alias_key, aliases in type_aliases.items():
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if poster_type in aliases or alias_key == poster_type:
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for alias in aliases:
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if alias in agents_by_type:
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agents = agents_by_type[alias]
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idx = used_indices.get(alias, 0) % len(agents)
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matched_agent_id = agents[idx].agent_id
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used_indices[alias] = idx + 1
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break
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if matched_agent_id is not None:
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break
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# 3. 如果仍未找到，使用影响力最高的 agent
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if matched_agent_id is None:
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logger.warning(f"未找到类型 '{poster_type}' 的匹配 Agent，使用影响力最高的 Agent")
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if agent_configs:
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# 按影响力排序，选择影响力最高的
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sorted_agents = sorted(agent_configs, key=lambda a: a.influence_weight, reverse=True)
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matched_agent_id = sorted_agents[0].agent_id
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else:
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matched_agent_id = 0
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updated_posts.append({
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"content": content,
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"poster_type": post.get("poster_type", "Unknown"),
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"poster_agent_id": matched_agent_id
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})
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logger.info(f"初始帖子分配: poster_type='{poster_type}' -> agent_id={matched_agent_id}")
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event_config.initial_posts = updated_posts
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return event_config
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def _generate_agent_configs_batch(
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self,
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context: str,
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@ -324,17 +324,30 @@ class SimulationManager:
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item_name=msg
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)
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# 设置实时保存的文件路径（优先使用 Reddit JSON 格式）
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realtime_output_path = None
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realtime_platform = "reddit"
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if state.enable_reddit:
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realtime_output_path = os.path.join(sim_dir, "reddit_profiles.json")
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realtime_platform = "reddit"
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elif state.enable_twitter:
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realtime_output_path = os.path.join(sim_dir, "twitter_profiles.csv")
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realtime_platform = "twitter"
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profiles = generator.generate_profiles_from_entities(
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entities=filtered.entities,
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use_llm=use_llm_for_profiles,
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progress_callback=profile_progress,
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graph_id=state.graph_id, # 传入graph_id用于Zep检索
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parallel_count=parallel_profile_count # 并行生成数量
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parallel_count=parallel_profile_count, # 并行生成数量
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realtime_output_path=realtime_output_path, # 实时保存路径
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output_platform=realtime_platform # 输出格式
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)
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state.profiles_count = len(profiles)
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# 保存Profile文件（注意：Twitter使用CSV格式，Reddit使用JSON格式）
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# Reddit 已经在生成过程中实时保存了，这里再保存一次确保完整性
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if progress_callback:
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progress_callback(
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"generating_profiles", 95,
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